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DAMASK_EICMD
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more conservative estimate

This commit is contained in:
Martin Diehl 2020-03-21 21:10:14 +01:00
parent 4decae967a
commit 2c1a654966
1 changed files with 5 additions and 5 deletions
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10
processing/pre/seeds_fromRandom.py
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@ -115,9 +115,9 @@ for name in filenames:
if options.N > np.prod(grid): if options.N > np.prod(grid):
damask.util.croak('More seeds than grid positions.') damask.util.croak('More seeds than grid positions.')
sys.exit() sys.exit()
if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5*np.prod(size): if options.selective and options.distance**3*options.N > 0.5*np.prod(size):
vol = 4./3.*np.pi*(options.distance/2.)**3 damask.util.croak('Number of seeds for given size and distance should be < {}.'\
damask.util.croak('Recommended # of seeds is {}.'.format(int(0.5*np.prod(size)/vol))) .format(int(0.5*np.prod(size)/options.distance**3)))
eulers = np.random.rand(options.N,3) # create random Euler triplets eulers = np.random.rand(options.N,3) # create random Euler triplets
eulers[:,0] *= 360.0 # phi_1 is uniformly distributed eulers[:,0] *= 360.0 # phi_1 is uniformly distributed
@ -138,8 +138,8 @@ for name in filenames:
i = 1 i = 1
progress = damask.util._ProgressBar(options.N,'',50) progress = damask.util._ProgressBar(options.N,'',50)
while i < options.N: while i < options.N:
candidates = np.random.choice(unpicked[not unpicked.mask],replace=False, candidates = np.random.choice(unpicked[np.logical_not(unpicked.mask)],replace=False,
size=min(len(unpicked[not unpicked.mask]),options.numCandidates)) size=min(np.count_nonzero(unpicked.mask),options.numCandidates))
tree = spatial.cKDTree(seeds[:i]) tree = spatial.cKDTree(seeds[:i])
distances, dev_null = tree.query(coords[candidates]) distances, dev_null = tree.query(coords[candidates])
best = distances.argmax() best = distances.argmax()